Academic Journals Database
Disseminating quality controlled scientific knowledge

Applications of the General Nonlinear Neural Networks in Solving the Inverse Optimal Value Problem with Linear Constraints

ADD TO MY LIST
 
Author(s): Huaiqin Wu | Kewang Wang | Ning Li | Chongyang Wu | Qiangqiang Guo | Guohua Xu

Journal: Information Technology Journal
ISSN 1812-5638

Volume: 11;
Issue: 6;
Start page: 713;
Date: 2012;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

Keywords: Neural networks | global asymptotical stability | fish-burmeister function | bilevel programming | inverse optimal value problem

ABSTRACT
In this research, the aim of the study is to present a method to solve a class of inverse optimal value problem with linear constraints by using a nonlinear gradient neural network. Firstly, based on optimal theory, solving the inverse optimal value problem is changed as solving a nonlinear bilevel program problem equivalently. Then a nonlinear gradient neural network model for solving the nonlinear bilevel programming is presented. By employing Lyapunov function approach, the proposed neural network is analyzed to be globally Lyapunov stable and capable of generating approximal optimal solution to the nonlinear bilevel programming problem. Finally, numerical examples are provided to verify the feasibility and the efficiency of the proposed method in this study.

Tango Jona
Tangokurs Rapperswil-Jona

     Save time & money - Smart Internet Solutions